Traditionally, Pattern Recognition systems have been designed to work in an automatic way,

taking the place of the manual human intervention. Based on this paradigm, multiple and diverse systems have been

implemented in different domains. We proposed a reorientation of the current research with a new paradigm

where the systems will not

perform in an automatic way but in an interactive fashion. The main reason for this is that automatic systems are not

free from errors and, being high quality results the principal objective, a kind of supervision is needed. On the other

hand, as time goes by, intrinsic interactive applications are more important and frequent. For example, information

retrieval applications or computer aided decision systems. The main objective of the present project is to make use of

the information provided by that supervision in order to train again the models and having the opportunity of correcting

the forthcoming errors. In that case, the efficiency of the system is measured as a combination of the effort provided by

the user during the learning task (usually the number of interactions needed to fulfil it) together with the time the system

needs to re-train their models and provide a new hypothesis. The interactive paradigm poses new challenges to the

Pattern Recognition research community: incremental models learning (dynamic algorithms), new strategies for

hypothesis generation, making use of the inner data consistency (unsupervised learning) and many more that will

appear during the development of the project. These new strategies will give rise to the development of new concepts

and techniques, as a result of applying this new paradigm on many different methodologies, classic as long as the most

recent ones in the filed of Pattern Recognition.

Another challenge of this project consist of particularizing the above ideas to several interesting application domains as

human behaviour analysis, text transcription, content-based image retrieval, handwriting recognition, surveillance,

access control and biometrics systems, materials inspection and stellar spectra classification.